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Author:

Wang, Z. (Wang, Z..) | Zhang, X. (Zhang, X..) | Wu, H. (Wu, H..) | Chadli, M. (Chadli, M..) | Huang, T. (Huang, T..) | Qiao, J. (Qiao, J..)

Indexed by:

EI Scopus SCIE

Abstract:

For nonlinear space-varying parabolic partial differential equation systems (PPDESs) with random time-varying delay, this paper introduces a dynamic fuzzy boundary output feedback (DFBOF) control under non-collocated boundary measurement (NCBM). Initially, the nonlinear delayed PPDESs are represented by Takagi-Sugeno (T–S) fuzzy models and random time-varying delay is considered by taking the influence of uncertain factors, which belongs to two intervals in a probabilistic way. Since the system state is not fully available and NCBM makes the boundary control design very difficult, a fuzzy observer under NCBM is presented to surmount the design difficulty. Subsequently, an observer-based fuzzy boundary controller is proposed and spatial linear matrix inequality (SLMI) based sufficient conditions to ensure mean square exponential stability are obtained for closed-loop delayed PPDESs by utilizing the Lyapunov direct method and Wirtinger inequality. Then, to solve the SLMIs, the feasibility conditions of DFBOF controller design for nonlinear delayed PPDES are expressed in LMIs. Lastly, two examples are offered to demonstrate the validity of the presented dynamic fuzzy boundary control approach. IEEE

Keyword:

Non-collocated boundary control Actuators Delayed parabolic PDE system Delays Observers Takagi-Sugeno (T–S) model Control design Random time-varying delay Sensors Decentralized control Process control Fuzzy control

Author Community:

  • [ 1 ] [Wang Z.]Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing Laboratory of Smart Environmental Protection, and Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China
  • [ 2 ] [Zhang X.]School of Electrical Engineering, University of Jinan, Jinan, China
  • [ 3 ] [Wu H.]Science and Technology on Aircraft Control Laboratory, School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
  • [ 4 ] [Chadli M.]Laboratoire IBISC, Universit'e Evry, Universit'e Paris-Saclay, Evry, France
  • [ 5 ] [Huang T.]Science Program, Texas A&M University, College Station, TX, USA
  • [ 6 ] [Qiao J.]Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing Laboratory of Smart Environmental Protection, and Beijing Institute of Artificial Intelligence, Beijing University of Technology, Beijing, China

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Source :

IEEE Transactions on Fuzzy Systems

ISSN: 1063-6706

Year: 2022

Issue: 6

Volume: 31

Page: 1-12

1 1 . 9

JCR@2022

1 1 . 9 0 0

JCR@2022

ESI Discipline: ENGINEERING;

ESI HC Threshold:49

JCR Journal Grade:1

CAS Journal Grade:1

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 10

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 5

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